Edge-based artificial intelligence enablement

ABSTRACT

An edge computing telecommunications network is provided for efficiently generating and updating computing models for use at distributed devices connected to different edge compute sites of the network. A network orchestration system may track devices connected to the network and the edge compute sites to which they are connected. The devices may comprise limited computing power and may include sensors or other data collection mechanisms. Raw data may be provided from connected devices to one or more edge compute sites. Edge compute sites may be instructed, e.g., by the network orchestration system, whether to replicate the raw data, modify the data to make it ready for consumption by a computing model, replicate the modified data, refine the computing model, replicate the refined computing model, and/or share some or all of the raw data, modified data, and/or refined computing model with other edge computing sites and/or connected devices.

BACKGROUND

Edge computing is a computing architecture in which computations and/ordata storage is performed physically and/or logically near a location ofan entity that requested these services. The proximity of the requestingcomputing device to the computing device(s) that perform thecomputations and/or data storage saves bandwidth and reduces latency.

SUMMARY

Examples of the present disclosure relate to an edge-basedtelecommunications network that enables efficient use ofartificial-intelligence and/or machine-learning models. For example, amethod is provided comprising: receiving, at a first edge compute siteof a telecommunications network, raw data from a first device over afirst access network; determining, by the first edge compute site,whether to send the raw data to a second edge compute site of thetelecommunications network; when it is determined to send the raw datato the second edge compute site of the telecommunications network,sending, by the first edge compute site, the raw data to the second edgecompute site; determining, by the first edge compute site, whether theraw data needs to be modified for consumption by a first model that isstored by one of the first edge compute site, the second edge computesite, the first device, or a second device connected to the first edgecompute site; when it is determined that the raw data needs to bemodified for consumption by the first model, modifying the raw data togenerate modified data; determining, by the first edge compute site,whether to provide the modified data to at least one of the second edgecompute site, the first device, or the second device; when it isdetermined to provide the modified data to at least one of the secondedge compute site, the first device, or the second device, providing themodified data to at least one of the second edge compute site, the firstdevice, or the second device; determining, by the first edge computesite, whether to modify the first model at the first edge compute siteusing the modified data; when it is determined to modify the first modelat the first edge compute site, modifying the first model using themodified data to generate a modified first model; determining, by thefirst edge compute site, whether to send the modified first model to atleast one of the second edge compute site, the first device, or thesecond device; when it is determined to send the modified first model toat least one of the second edge compute site, the first device, or thesecond device, sending the modified first model to at least one of thesecond edge compute site, the first device, or the second device; andusing the modified first model to automatically affect operation of atleast one of the first edge compute site, the second edge compute site,the first device, or the second device.

In other examples, a system is provided comprising at least oneprocessor and memory, operatively connected to the at least oneprocessor and storing instructions that, when executed by the at leastone processor, cause the system to perform a method. In examples, thatmethod may comprise: receiving, at a first edge compute site of atelecommunications network, raw data from a first device over a firstaccess network; determining, by the first edge compute site, whether tosend the raw data to a second edge compute site of thetelecommunications network; when it is determined to send the raw datato the second edge compute site of the telecommunications network,sending, by the first edge compute site, the raw data to the second edgecompute site; determining, by the first edge compute site, whether theraw data needs to be modified for consumption by a first model that isstored by one of the first edge compute site, the second edge computesite, the first device, or a second device connected to the first edgecompute site; when it is determined that the raw data needs to bemodified for consumption by the first model, modifying the raw data togenerate modified data; determining, by the first edge compute site,whether to provide the modified data to at least one of the second edgecompute site, the first device, or the second device; when it isdetermined to provide the modified data to at least one of the secondedge compute site, the first device, or the second device, providing themodified data to at least one of the second edge compute site, the firstdevice, or the second device; determining, by the first edge computesite, whether to modify the first model at the first edge compute siteusing the modified data; when it is determined to modify the first modelat the first edge compute site, modifying the first model using themodified data to generate a modified first model; determining, by thefirst edge compute site, whether to send the modified first model to atleast one of the second edge compute site, the first device, or thesecond device; when it is determined to send the modified first model toat least one of the second edge compute site, the first device, or thesecond device, sending the modified first model to at least one of thesecond edge compute site, the first device, or the second device; andusing the modified first model to automatically affect operation of atleast one of the first edge compute site, the second edge compute site,the first device, or the second device.

In other examples, a method is provided comprising: determining, by anetwork orchestration system of an edge telecommunications network, afirst set of one or more edge compute sites currently connected to atleast one device utilizing a first model; determining, by the networkorchestration system of the edge telecommunications network, a secondset of one or more edge compute nodes currently connected to at leastone device utilizing a second model; providing, by the networkorchestration system, first instructions to the first set of one or moreedge compute sites, the first instructions comprising: whether and whereto replicate raw data received from the at least one device utilizingthe first model; whether to modify the raw data at the first set of oneor more edge compute sites to generate first modified data; whether andwhere to replicate the first modified data to one or more other edgecompute sites in the first set; whether to modify the first model at thefirst set of one or more edge compute sites to generate a first modifiedmodel; and whether and where to replicate the first modified model. Inexamples, the method may also comprise: providing, by the networkorchestration system, second instructions to the second set of one ormore edge compute sites, the second instructions comprising: whether andwhere to replicate raw data received from the at least one deviceutilizing the second model; whether to modify the raw data at the secondset of one or more edge compute sites to generate second modified data;whether and where to replicate the second modified data to one or moreother edge compute sites in the second set; whether to modify the secondmodel at the second set of one or more edge compute sites to generate asecond modified model; and whether and where to replicate the secondmodified model.

This summary is provided to introduce a selection of concepts in asimplified form that are further described below in the DetailedDescription. This summary is not intended to identify key features oressential features of the claimed subject matter, nor is it intended tobe used to limit the scope of the claimed subject matter.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic diagram illustrating an edge telecommunicationsnetwork system in accordance with one embodiment.

FIG. 2 is a schematic diagram illustrating an edge compute environmentof an edge site of a network in accordance with one embodiment.

FIG. 3A and FIG. 3B are a flowchart illustrating a method for enablingdevices using a model and connected to an edge compute network tooperate.

FIG. 4 is a block diagram illustrating an example of a computing deviceor computer system.

DETAILED DESCRIPTION

Models, such as artificial intelligence models, continuously improvethrough data consumption. For example, a self-driving model will berefined from data obtained from a moving vehicle using the model. Themodel may use data such as images captured while the vehicle moves,sensor data, engine data, and other data. There can be a large amount ofdata available to refine a model. Additionally, the data may need to bemodified before the model can use the data. Devices using a model maynot have the ability to collect and store the large amount of data fastenough, if at all, to efficiently refine the model. Similarly, thedevices using a model may not have the ability to modify or refine thedata for the model's use. Models that do not improve quickly enough mayfail. For example, a vehicle using a self-driving model that cannotquickly improve the model itself or cannot quickly communicate withother devices that collect data, modify data, and/or improve the model,due to high latency, may crash or otherwise operate incorrectly. Modelsused by devices may also improve inefficiently if data collection islimited. For example, a self-driving model that only accesses dataassociated with a single vehicle will not improve as quickly orefficiently as a self-driving model that accesses data associated with alarge number of vehicles. In other examples, the model may comprise amodel used to predict failures of network computing cards in a server orother computing device. For example, an organization may own or controlthousands of computing devices within a computing system, and theorganization may benefit from generating and refining models used topredict computing system failures before they occur so that remedialmeasures can be taken.

Communications networks may provide many services to customers and/ordevices associated with customers of the network, including transmissionof communications between network devices, network services, networkcomputing environments, cloud services (such as storage services,networking service, compute services, etc.), and the like. To provideservices such as data collection, data modification, and model refining,networking components and other devices are interconnected andconfigured within the network such that devices may access thecommunications network. Edge sites of the communications network may bein many locations to lower the latency when a device accesses thenetwork. The edge sites may allow data to be collected from devices,allow the data to be shared to other sites, allow the data to bemodified for use by models, and allow for the models to be refined foruse by the devices, all with low latency. The communications network mayhave much more processing power than the individual devices using thenetwork to collect and process data quickly.

Aspects of the present application describe an edge telecommunicationsnetwork system that can enable devices using models such asartificial-intelligence/machine-learning models to operate. The edgetelecommunications network system may collect data from devices incommunication or otherwise connected to the edge telecommunicationsnetwork system such as via one or more edge compute sites of the system.The collected data may be shared with other edge sites so deviceslocated anywhere and communicating with the system will have access tothe same data. The collected data may be modified so that models can berefined by using the modified data. The modified data may be used torefine models stored on the system or may be accessible to the devicesto refine models not stored on the system, such as stored on the devicesthemselves.

The edge telecommunications network system can enable devices usingmodels such as artificial intelligence models to operate using datalayers. For example, the edge telecommunications network system may usea four-tier data model, including a data transport layer, a data sessionlayer, a data presentation layer, and a data application layer. The datatransport layer defines participants of the network and manages thecommunication infrastructure. The data session layer replicates datausing a distributed data tier. For example, the data session layer maycause the edge sites of the edge telecommunications network system tocontinuously/periodically communicate and/or replicate data to maintaindata concurrency throughout the network. The data presentation lateringests data such as data received from devices communicating with orotherwise connected to the edge telecommunications network system, andthe data presentation layer modifies data. In one example, the data ismodified to be ready for use to refine an artificial-intelligence ormachine-learning model. The data presentation layer can modify the databased on the model(s) that will use the data and the use cases andintents of the model(s). The data application layer is the set ofmicroservices and Application Programming Interfaces (APIs) thatinteract with the devices. The edge telecommunications network systemmay provide or otherwise cause the microservices and APIs to beaccessible by the devices connected to the edge telecommunicationsnetwork system.

The edge telecommunications network system may also include anorchestration system. The orchestration system can communicate with andcontrol edge sites of the edge telecommunications network system. Theorchestration system can configure and provision the edge sites andestablish how the edge site should function. The orchestration systemcan provide rules or other instructions to the systems implementing theindividual data layers to direct those systems how to function. Forexample, the orchestration system may instruct the transport layersystem of the edge sites which other edge sites to share/replicate datawith.

FIG. 1 is a schematic diagram illustrating an edge telecommunicationsnetwork system 100 in accordance with one embodiment. In general, theedge telecommunications network system 100 may include edge computesites 102 a-n and an orchestration system 114. Each edge compute site102 a-n may provide compute, data, and capability services to devices,such as devices 120 a-n, connected or otherwise in communication withthe edge compute site. In some embodiments, the edge compute sites 102a-n operate according to the data layer constructs described above, butFIG. 1 illustrates the data layers as systems in the present embodiment.In examples, the systems 104, 106, 108, and 110 may be separate systemsand/or may be combined. For example, transport system 104 and sessionsystem 106 may be implemented by a single database replication system.The edge compute sites 102 a-n may be in different geographic locationsof the edge telecommunications network system 100 to reduce the latencyof providing services to devices in communication with or otherwiseconnected to the edge telecommunications network system 100. Forexample, a device, such as device 120 c or device 120 d, may be locatednear edge compute site 102 b and receiving services will therefore befastest when provided by edge compute site 102 b. Device 120 c or device120 d receiving the same services from edge compute site 102 a or edgecompute site 102 n may be slower due to the greater distances of theedge compute sites from the device. The device, such as device 120 c ordevice 120 d, may communicate or otherwise connect to the edge computesites, such as edge compute site 102 b, via networks 112 a-n. Thenetworks 112 a-n can include one or more data communication networks,such as the Internet, private networks, cellular data communicationnetworks, local area networks, and the like. The interactions andcommunications between the components of the edge telecommunicationsnetwork system 100 is described in more detail herein. It should beappreciated that an edge telecommunications network system may includemore or fewer components than those illustrated in FIG. 1 and may beconnected in other configurations than shown. Rather, the system 100 ofFIG. 1 is but one example of an edge telecommunications network system100 for providing compute, data, and capability services to devices ornetworks connected to or otherwise in communication with the edgecompute system.

In examples, devices 120 a-n may change which edge compute site tocommunicate with to connect to the edge telecommunications networksystem 100. For example, the device may be a moving vehicle or beconnected to the moving vehicle (e.g., vehicle 118). The device, such asdevice 120 c may have originally communicated with or otherwiseconnected to edge compute site 102 n because it was closest to and hadthe lowest latency when communicating with edge compute site 102 n. Asthe vehicle moved, device 120 c moved away from edge compute site 102 nand closer to edge compute site 102 b. Therefore, device 120 c starts tocommunicate with edge compute site 102 b, which now has the lowestlatency when communicating with device 120 c, rather than edge computesite 102 n. Devices communicating with the edge telecommunicationsnetwork system 100 can continuously change which edge compute site thedevices communicate with, but some devices may be stationary and alwayscommunicate with the same edge compute site. In other examples, thedevices 120 a-n may comprise mobile computing devices (such as wirelessphones, laptops, tablets, etc.). In other examples, the devices 120 a-nmay comprise computing servers or other computing devices at anorganization's office locations, each of which may connect to adifferent (logically closest) edge site 102 a-n within edgetelecommunications network system 100.

In examples, the edge telecommunications network system 100 storesand/or maintains one or more models such as an artificial-intelligence(AI) model or machine-learning (ML) model. In some examples, the one ormore models are generated and/or stored at one or more of the edgecompute sites 102 a-n in model systems 116 a-n. For example, in Python,a binary representation of a model may be stored in model systems 116a-n. The models can be provided to devices in communication with orotherwise connected to the edge telecommunications network system 100.The models may be generated, refined, and/or stored at the edge computesites 102 a-n in model systems 116 a-n to reduce the latency ofproviding the models or model updates to the devices. Each model may bestored at one of the edge compute sites based on whether a device thatcommunicates with the specific edge site uses the model. For example, adevice may communicate with edge compute site 102 b to connect to theedge telecommunications network system 100. In an example, the device,such as device 120 c, is a vehicle control unit that uses a drivingassist model. Model system 116 f may store the driving assist model forvehicle 118 to use. Device 120 c may be a vehicle-to-everything (V2X)communication device that allows vehicle 118 to communicate with theedge telecommunications network system 100 or to other V2X devices that,themselves, pass data to and from the edge telecommunications networksystem 100. Device 120 c may also collect data such as from cameras,vehicle sensors, and/or other vehicles or devices monitoring theoperation of the vehicle to subsequently send the collected data to theedge telecommunications network system 100.

In some examples, certain model(s) are stored in the orchestrationsystem 114 and specifically in model system 116 c. For example, theorchestration system 114 may receive raw data or modified data from oneor more of the edge compute sites 102 and use it to modify the modelstored in model system 116 c. In other examples, the model may begenerated and refined by the edge compute site(s) 102 before beingprovided to the orchestration system 114. In further examples, themodel(s) are created, stored, and refined on the devices 120. Forexample, the model(s) that each device 120 a-n use are stored in modelsystems 116 d-i. The devices may communicate with the edgetelecommunications network system 100 to receive data that will be usedto refine the model(s) stored in model systems 116 d-i. For example,device 120 c may receive data from the edge telecommunications networksystem 100. The data may be data collected by device 120 c (and refinedby edge telecommunications network system 100 for consumption by themodel system 116 f) and/or from other devices communicating with theedge telecommunications network system 100. The data may also bemodified to be used by the model (such as a self-driving model) storedin model system 116 f. As used herein, modified data (or refined data)means raw data received from a device 120 and modified by presentationsystem 108 in order to extract features or normalize the raw data foruse in one or more model systems 116. Model system 116 f can use themodified data received from the edge telecommunications network system100 to refine the stored self-driving model.

The edge compute sites 102 a-n may receive data from devices connectedto the edge telecommunications network system 100. Each device maycommunicate with and send data to one or more of the edge compute sites102 a-n such as by networks 112 a-n. For example, each device 120 maycommunicate with and send data to the edge compute site 102 having thelowest latency to communicate with and send data to. The received datamay include data necessary to enable and/or refine models used by thedevices and/or capabilities of the devices. For example, a deviceconnected to the edge telecommunications network system 100 may senddata to be used to update a model to ensure that the device and/orsystems in communication with and/or controlled by the device operateproperly.

In an example, device 120 a is a system controlling an oil drill andresponsible for preventing the drill from overheating. Device 120 a maycollect data (e.g., sensor data about the oil drill such as the drillspeed, the soil characteristics, the operating temperature, and so on)and send it to edge compute site 102 a. The edge telecommunicationsnetwork system 100 can use the data to continuously update and/or refinethe model used by the device and/or capabilities of the device.Additionally, the data may be used to update and/or refine the modelsused by other devices and/or the capabilities of other devices connectedto the edge telecommunications network system 100. For example, device120 a may send data collected when an oil drill overheats. The data canbe used to refine the model, such as to prevent the drill (or similardrills) from failing for the same or similar reasons. The refinedartificial-intelligence and/or machine-learning model can then be usedby other devices, such as device 120 b, device 120 e, and so on, so theoil drill(s) the devices control do not overheat in the future for thesame or similar reasons. Alternatively, orchestration system 114 maydetermine that the model (and the data used to refine the model) neednot be replicated because it is specific only to the device 120 a, whichis stationary and always communicates with edge compute site 102 a (andno other sites). As such, the orchestration system 114 may instruct theedge compute site 102 a not to replicate or send the data to any of theother edge compute sites 102 b-n. In other examples, the edge computesites 112 may communicate directly with one another to subscribe and/orunsubscribe to receive updates of relevant raw data, modified data,and/or refined/updated models.

In an example, the edge compute sites 102 a-n do not refine the model;rather, they replicate either raw or modified data that is then consumedby model systems 116 d-i. For example, the data collected by device 120a is received by edge compute site 102 a and shared with edge computesite 102 n. In one example, the data is modified, such as bypresentation system 108 a and/or presentation system 108 n, as will bedescribed in more detail herein. The edge compute sites may then sharethe data with the devices so individual models stored on devices 120 canbe refined. For example, the data shared by device 120 a regarding theoil drill overheating can be shared with edge compute site 102 n andmodified by presentation system 108 n for consumption by device 120 e.The modified data may then be sent to device 120 a and device 120 e. Themodified data can then be used by model system 116 d and model system116 h to refine the model that is used by device 120 a and device 120 eto prevent oil drills from overheating. Device 120 e may also send datato edge compute site 102 n. The data may then be modified and sharedwith device 120 a and device 120 e for model system 116 d and modelsystem 116 i to refine the model used to control oil drills again.

Each edge compute site 102 a-n may include a transport system 104 a-n.The transport systems 104 a-n may determine the edge compute site(s)that each edge compute site should share data with or otherwise connectto. In examples, the transport systems 104 a-n determine which edgecompute site(s) the edge compute sites 102 a-n should replicate data tobased on the devices connected to each edge compute site 102 and thedata in question. For example, edge compute site 102 a may connect andreplicate data to both edge compute site 102 b and edge compute site 102n because each edge compute site is communicating with devices that usethe same model or a similar model. In an example, the transport systems104 a-n comprise database systems powered by software like HarperDBprovided by HarperDB Inc. For example, transport systems may comprisedatabase systems that permit operational technology systems (such assensors, monitoring systems, etc.) to easily integrate their data withinformation technology systems (such as event logs).

In examples, the device 120 c may be connected to or part of vehicle118. A vehicle is traditionally an operational technology (OT)environment comprised of multiple sensors collecting various types ofdata used to enable the driver or pilot to manage the vehicle moreeffectively. In examples, the device 120 c may comprise an onboarddiagnostic II (ODB2) interface connected to a Raspberry Pi runningHarperDB to collect and transmit the data using native Harper DB tonetwork 100. Harper DB is a lightweight, highly scalable hybriddatabase, small enough to run on a micro controller in a supervisorycontrol and data acquisition (SCADA) environment, and scalable enough tohandle petabytes of data in a deployment on network 100. Because thisallows data from sensors, controllers, and syslog servers to be nativelyingested, it can be used as a portable data abstraction layer. Raw datacan then be retrieved from nearly any device, regardless of protocol orinterface, and exposed to the presentation system 108 for ingestion. Thetransport systems 104 can also act as a data replicationengine—providing a reliable data transport in unreliable, changingnetwork conditions by implementing it as an edge data persistence layerand allowing it to find a reliable network transport, holding the datauntil the network becomes available. For example, the lightweightdatabase can be deployed in vehicles and using adhoc networking tocollect and transmit data from remote locations. An edge compute site102 can pick up data from a remote data node or device as the transportnode in a vehicle drives past a warehouse with a remote data node. Inexamples, the vehicle transports the data to the remote data node eitherover cellular from inside the vehicle or via wifi when it comes in rangeof a paired wifi infrastructure.

The transport systems 104 may also determine what specific datacollected from the devices connected to the edge telecommunicationsnetwork system 100 to share with other edge compute sites. For example,assume edge compute site 102 a and edge compute site 102 b bothcommunicate with and connect to devices that control oil drills. Edgecompute site 102 a receives data about a drill overheating from a devicevia network 112 a. The transport system 104 a determines to send thereceived data to edge compute site 102 b so the data can be sent todevices in communication with edge compute site 102 a and with edgecompute site 102 b. The devices 120 use the data to refine one or moremodel(s), such as a model stored in model systems 116 d-g, used by thedevices to prevent the oil drill(s) from overheating. Alternatively, thetransport system 104 a determines to send the received raw data to edgecompute site 102 b so both edge compute sites 102 a and 102 b can refinethe model. In this example, edge compute site 102 n may not communicatewith any devices that prevent oil drills from overheating. Thus, eventhough the transport system 104 a previously determined that edgecompute site 102 a and edge compute site 102 n should share data, thetransport system 104 a determines that the received data should not besent to the edge compute site 102 n because edge compute site 102 n doesnot communicate with any devices that use the data. In other examples,the data is shared with edge compute site 102 n to maintain dataconsistency and/or so it is available if a device connects to edgecompute site 102 n in the future and needs the data. Additionally, adevice connected to edge compute site 102 n may begin using a model thatuses the data.

The transport systems 104 a-n may group devices that use a similar modelto determine when an edge compute site 102 should share data. Inexamples, the transport systems 104 a-n track when devices switch tocommunicating with a different edge compute site, and when devices startand stop using a model to determine which edge compute sites shouldshare data with other edge compute sites. The groups may be continuouslyupdated so the edge compute sites share data only with edge computesites that need it. In examples, orchestration system 114 may provideinstructions to the transport systems 104 a-n to instruct the transportsystems 104 a-n as to particular data-replication groups. As mentionedabove, some devices are stationary and always communicate with the sameedge compute site. In examples, the transport systems 104 a-n may notwaste resources tracking stationary devices to determine whether thedevice is communicating with a different edge compute site.

In another example, edge compute site 102 b and edge compute site 102 nmay both communicate with devices that utilize a driving assist model,and edge compute site 102 a may not communicate with any devices thatutilize a driving assist model. In this example, transport system 104 bmay determine that any collected data related to the driving assistmodel should be shared with edge compute site 102 n and not with edgecompute site 102 a, and transport system 104 n will determine that anycollected data related to the driving assist model should be shared withedge compute site 102 b and not shared with edge compute site 102 a. Thetransport systems 104 b may determine that any number of edge computesites should receive data for each model the edge compute site collectsrelated data. In examples, the transport systems store the collection ofedge compute sites that should receive data for each model for futurereference. This allows the transport systems 104 to avoid having todetermine which edge compute sites should be sent data every time datais received. The transport systems may update the stored collectionsperiodically.

The transport systems 104 a-n may still share data with other edgecompute sites that do not communicate with any devices using themodel(s) related to the collected data. For example, the transportsystems may determine that an edge compute site is likely to connect todevices that will use the model(s) in the future. The transport systemsmay additionally share data with only a subset of the edge computesites. For example, each of the transport systems may cause each edgecompute site to only share data with edge compute sites within ageographic area. As the edge compute site shares data with the edgecompute sites in the geographic area, the other edge compute sites cansend the data to other edge compute sites that are determined to needthe data. For example, edge compute site 102 a may send data to edgecompute site 102 b, and edge compute site 102 b will subsequently sendthe data to edge compute site 102 n. In an example, the orchestrationsystem 114 instructs the transport systems as to each edge compute sitethat should share data within a geographic area. The orchestrationsystem 114 can cause the transport systems to determine which edgecompute sites should be communicating based on other variables. Forexample, the orchestration system 114 can cause the transport systems todetermine which edge compute sites to communicate with based on the sizeof the component(s) of the edge compute sites, latency between the edgecompute sites, types of connections between the edge compute sites, andso on.

The transport systems 104 a-n may additionally determine which devicesto share the received data with. For example, the transport systems 104a-n may determine which devices use the same model(s) and whether thedevices should receive the data, the modified data (e.g., featurepairs), and/or the refined model(s). The transport systems 104 a-n mayalso protect the data, such as preventing specific portions of the datafrom being shared directly to devices as will be described in moredetail herein.

In addition, the orchestration system 114 may instruct the transportsystems 104 a-n whether to replicate between them raw data received fromdevices 120 a-n or modified data that has been processed by apresentation system 108 a-n. For example, in some instances,orchestration system will track the feature pairs that are being used bythe models being maintained on the system 100. For example, presentationsystems 108 may report to the orchestration system 114 which featurepairs are being extracted for devices connected to the applicable edgecompute site 102. If two edge compute sites 102 are utilizing the samefeature pairs for a particular data type, then orchestration system 114may instruct only modified data (e.g., the output of presentation system108) to be replicated between the edge compute sites. In this manner,computing and network resources are saved by not unnecessarilyreplicating all raw data. However, in some instances, different devicesand/or models may require that different feature pairs be extracted fromthe same raw data, in which case the orchestration system may instructthe transport systems to cause replication of raw data received fromdevices 120 a-n to one or more edge compute nodes 102 that are utilizingthat raw data.

The edge compute sites 102 a-n may also include session systems 106 a-n.The session systems 106 a-n replicate data that the edge compute sites102 send to other edge compute sites 102. The session systems 106 a-nmay use a distributed data tier to replicate the data. The sessionsystems 106 a-n may also ensure that the edge compute sites areconstantly or periodically communicating to maintain concurrency betweenthe edge compute sites. In an example, the session systems 106 a-n aredatabase systems that are powered by software such as HarperDB. Thesession systems 106 a-n may track the data sent to other edge computesites 102 and received from other edge compute sites 102. In examples,the session systems track the data sent and received to maintain theconsistency of the data accessed by each edge compute site. For example,edge compute site 102 a receives data from edge compute site 102 n.Session system 106 a determines that edge compute site 102 b has notreceived the data from edge compute site 102 n but should receive thedata. The session system 106 a causes the edge compute site 102 a toreplicate and send the data to edge compute site 102 b. The sessionsystem 106 n may also determine that edge compute site 102 a sent thedata to edge compute site 102 b so edge compute site 102 n does not needto send the data. The session systems ensure that each edge sitereceives the data it should so that each edge site can consistently anduniformly update the related model(s) and/or provide the data to devices120 so the devices 120 can consistently and uniformly update the relatedmodel(s).

As discussed, the edge compute sites 102 a-n may also includepresentation systems 108 a-n. In examples, the presentation systems 108a-n may comprise graphics processing unit (GPU) enabled databases. Inone example, the presentation systems 108 a-n are SQream databasesprovided by SQream Technologies Ltd. GPU enabled databases allow thepresentation systems 108 a-n to ingest and process large amounts of data(e.g., petabytes of data) continuously. The presentation systems 108 a-npackage and/or conceptualize the data received from devices. Forexample, the model(s) may not be able to be refined if the received datais directly provided to the model(s).

The presentation systems 108 a-n package and/or conceptualize the datafor the data to be usable to update the model(s), such as by extractingfeature pairs that have an input object and desired output value fromthe data, transforming the data into normalized form, and/or by removingirrelevant or unnecessary data. In examples, the presentation systems108 a-n continuously consume the received data and refine the data tomake it useable for the model(s). In an example, once raw data isreplicated and sent to each edge compute site that needs the data, thepresentation systems 108 a-n can extract multiple feature pairs from thedata for different applications and models. Each edge compute site thatis processing the data may be applying it to different applicationand/or models, so each presentation system 108 a-n may extract uniquefeature pairs. The feature pairs that are extracted may be based on thedevices in communication with the edge compute site and/or the model(s)that will use the data. The presentation systems 108 a-n may refine thedata (e.g., extracting feature pairs) based on previous data, themodel(s), the use case(s) of the model(s), and/or the objective(s) ofthe model(s). In examples, the presentation systems 108 a-n extractfeature pairs from the data based on request(s) from one or moreapplications (e.g., microservices, APIs) operating in the applicationsystems 110 a-n. The applications may be responsible for creating and/orrefining model(s) or for gathering modified data that is then used bymodel systems 116 d-i on devices 120 a-n to refine model(s).

In examples, the transport systems 104 a-n cause the edge compute sites102 a-n to send the modified data output from the presentation system108 to other edge compute sites for the model(s) to be updated. Thus,the transport systems of the other edge compute sites receiving the datado not need to modify the data. In examples, the edge compute sites maysend the modified data to devices that the transport systems determinedshould receive the data. The devices may use the modified data to updatemodel(s) stored on the devices.

In an example, the received data comprises images captured by a devicethat uses an image recognition model. The device may be continuouslycapturing and sending images to the edge telecommunications networksystem 100 from multiple image capture devices. The device 120 a maysend the data to edge compute site 102 a. The images are differentresolutions, have different lighting, and other different attributes.The presentation system 108 a refines the received images so the imagerecognition model can be refined and/or operate correctly. For example,the image recognition model may require each image to be 400 by 400pixels with uniform lighting. The presentation system 108 a can processthe images such that each image is 400 by 400 pixels and has uniformlighting. The modified data can then be used by edge compute site 102 aand/or the device(s) 120 to refine and/or operate the model.

The edge compute sites 102 a-n may also include application systems 110a-n. The application systems 110 a-n may comprise content managementsystems that include applications, microservices, and/or APIs thatinteract with the devices 120 communicating with or otherwise connectedto the edge telecommunications network system 100. In an example, theapplication systems 110 a-n are platform-based services such as Heroku,provided by Heroku, Inc. and Salesforce.com, Inc. In examples, theapplication systems 110 a-n store the model(s) that are provided to thedevices communicating with or otherwise connected to the edgetelecommunications network system 100. In other examples, theapplication systems 110 a-n manage data for the model(s) that are storedon the model systems 116 and/or stored on the devices connected to theedge telecommunications network system 100. Additionally, theapplication systems 110 a-n may use the received data and/or the datamodified by the presentation systems 108 a-n to update the model(s). Forexample, the application systems 110 a-n may use the feature pairsextracted by the presentation systems 108 a-n to refine one or moremodels. In another example, an application system, such as applicationsystem 110 a, provides an API to a device and the received data and/ormodified data via network 112 a so the device 120 a can update a modelstored on the device 120 a. The data sent to the device 120 a may bedata received from the device 120 and refined by presentation systems108. Additionally, the data sent to the device 120 a may be datareceived from a different device 120 b-n. The data received from thedifferent device 120 b-n may also be refined by one of the presentationsystems 108 b-n before the data is sent to the edge compute site 102 aand/or device 120 a.

The edge telecommunications network system 100 also includes anorchestration system 114. In an example, the orchestration system 114communicates with the edge compute sites 120 of the edgetelecommunications network system 100. The orchestration system 114 maybuild and manage the edge compute sites 120 and may also manage AI/MLmodels that are used to control the system 100 as a whole. For example,the orchestration system 114 defines how the transport systems 104 a-nshould determine to communicate with or otherwise connect with eachdevice, what data should be received and/or shared, what devices shouldshare data, data protection requirements, and so on. The orchestrationsystem 114 may determine which edge compute sites 102 will be grouped tocommunicate and share data and the rules that apply when sharing thedata. The orchestration system 114 can send the groupings and the rulesto the transport systems 104 for the transport systems to implement. Therules the transport systems 104 a-n should follow can be defined and/ormaintained within the orchestration system 114 and instantiated on thetransport systems 104 a-n. The rules may be updated by the orchestrationsystem 114 and sent to the transport systems for implementation so theoutdated groupings and rules are no longer followed.

The orchestration system 114 may determine the grouping of edge computesites 102 that will communicate based on the type of data. For example,edge compute sites 102 located in the United States (US) may be groupedto share data gathered by vehicles traveling in the US. The data may beused by the US edge sites to refine self-driving models specific to USroads and US driving rules. Therefore, the orchestration system 114 maynot include edge compute sites 102 outside the US or that do notcommunicate with devices located in the US in the US driving data group.The orchestration system 114 may additionally set rules regardingwhether the data should be sent to other edge compute sites 102 beforethe data is modified by the presentation systems or after. For example,the edge compute sites in a group are maintaining the same model andwill use the data in an identical way to refine the model. Theorchestration system 114 may establish a rule that the presentationsystem of the edge compute site that receives the data should modify thedata and the edge compute site 102 should replicate and send themodified data to the other edge compute sites 102. This prevents thepresentation systems 108 of the other edge compute sites 120 fromunnecessarily performing the same work as the first presentation system108. The rule may also optimize bandwidth used when transporting thedata and/or reduce the latency when sending the data. In anotherexample, the edge compute sites 102 may use the models in differentways. The orchestration system 114 may implement a rule that the rawdata received from devices 120 should be sent to certain of the edgecompute sites 102 instead of data modified by a presentation system 108.The orchestration system 114 may establish a combination of the rulesfor each edge compute site 102 to optimize the amount of work thepresentation systems 108 are responsible for.

The edge telecommunications network system 100 may protect the data thatis received. For example, the edge telecommunications network system 100may tag each element of the received data with a security value. Inexamples, the transport systems 104 and/or the presentation systems 108tag the received data with the security value. The security value may bebased on the characteristics of the data, such as whether the dataincludes sensitive information and whether the data has potential forabuse. The security value may also be based on whether the data can beused with sensitive parts of the data being obfuscated or otherwiseremoved. Once the data is modified by presentation system, if thesensitive parts of the data are not included, a low security value canbe assigned. For example, the presentation system may extract featurepairs from the data that do not include sensitive information. Thefeatures pairs can be assigned a low security value and be sent todevices since the sensitive data is excluded. In examples, when thesensitive information cannot be removed, devices 120 may be restrictedfrom receiving the data. The model may be refined by the edge computesite(s) 102 and then provided to the device(s) 120. Therefore, thedevices 120 can access the refined model without accessing theinformation. In other examples, only devices 120 with authorization toreceive data having the security value may receive the data.

The security value may also be represented when the data is modified bythe presentation systems 108 to extract feature pairs for models toingest or otherwise utilize. In examples, artificial-intelligence and/ormachine-learning models use feature pairs determine measurableproperties and characteristics of the data. The feature pairs may beused to determine what uses of the data are acceptable. The edgetelecommunications network system 100 may also monitor the data todetermine how the data should be used. In an example, the presentationsystems 108 monitor the data. In an example, there may be a monitoringpolicy that defines the acceptable uses of the data. The data may bemonitored to determine whether the data may be used to refine modelsstored on the model systems 116 of the edge compute sites 102, sentdirectly to devices 120, and so on.

The application layer or application system 110 may use an identityaccess management capability to determine how the data can be accessedand by which devices. For example, the application system 110 maydetermine whether humans can access data directly or if the data shouldbe accessible only by automated processes. The application system 110may also determine whether the data is related to a public or privateedge service. If the data is related to a private edge service, the datamay be shared only with devices having permission to access data relatedto the private edge service.

The data may also be protected by restricting access to data. Forexample, if a model is refined autonomously, the restrictions may belower than if data can be accessed by a person. In examples, theapplication systems 110 protect the data with an identity accessmanagement capability. The identity access management capability candetermine which devices 120 and/or user can access which data.

FIG. 2 is a schematic diagram illustrating an edge compute environment200 of an edge site of a network in accordance with one embodiment. Ingeneral, the edge compute environment 200 of FIG. 2 illustrates oneexample of components of an edge compute site 102 of a network orcollection of networks 202 a-202 c from which compute, data, andcapability services may be provided to devices connected or otherwise incommunication with the edge site. As mentioned above, by providing theenvironment 200 in an edge site of the network 202, compute, data, andcapability services may be provided to devices with a lower latency thanif the compute environment is included deeper within the network orfurther away from the requesting device of the network. It should beappreciated, however, that an edge compute system may include more orfewer components than those illustrated in FIG. 2 and may be connectedin other configurations than shown. Rather, the edge compute environment200 of FIG. 2 is but one example of an edge compute system 200 forproviding compute services to devices or networks connected to orotherwise in communication with the edge compute system.

In the instance shown, the components of the system 200 may be installedor associated with a network site at the edge of one or more networks202 a-c. In general, an edge site of a network is a network site inwhich devices such as customer equipment may connect to the network 202for access to services and transmission routes of the network. Furtherand as discussed above, the network 202 may include more than one publicand/or private network interconnected to form a general network 202.Each network instance may include one or more edge devices 204 thatprovide gateways or ingress/egress devices for the associated network.In FIG. 2 , network 202 a may include edge devices 204 a, network 202 bmay include edge devices 204 b, and network 202 c may include edgedevices 204 c. Each edge device 204 of the networks 202 may connect orotherwise communicate with one or more spine switch devices 206 a-b. Oneor more host leaf switches 208 a-b may interconnect with the one or morespine switch devices 206 a-b of the environment 200 to form a switchmesh for connecting to the network 202 via edge devices 204. In someinstances, more or fewer spine switch devices 206 and/or host leafswitches 208 may be included in the edge compute environment 200.Further, each spine switch 206 and host leaf switch 208 may provideredundancy failover services for a corresponding switch.

One or more bare metal servers 210 a-n or other types of servers may beconnected to each host leaf switch 208. In one implementation, theservers 210 may host and execute applications to provide particularservices to devices and customers of the network 202. For example, theservers 210 may be configured to provide compute services (as well asother cloud computing services) to customers in communication with theservers 210. In another example, the servers 210 may be configured toprovide data and capability services to enable artificial intelligencecapabilities of devices in communication with servers 210. Further,although 16 such servers are illustrated in FIG. 2 , the environment 200may include more or fewer servers 210 for providing services tocustomers. The environment 200 may also include a host management switch212 connected to the host leaf switches 208 for managing aspects of theswitching mesh and communications to/from the servers 210. Through theenvironment 200 of FIG. 2 , an edge compute service may be provided todevices and customers of the network 202 requesting such services fromthe network 202 while reducing the latency of providing the services tothe devices and customers.

FIG. 3A and FIG. 3B comprise a flowchart illustrating a method 300 forenabling devices using a model and connected to an edge compute networkto operate. In some implementations, one or more of the operations ofthe method 300 may be performed by the edge telecommunications networksystem 100. In other implementations, one or more of the operations maybe performed by other components of the edge compute sites 102 or stillother systems. The operations may be executed by hardware components ofthe relevant systems, software programs of the systems, or a combinationof hardware and software components of the system. Some operations maybe combined into a single operation or performed in a different orderthan illustrated in method 300.

Beginning in FIG. 3A, flow starts at operation 302. In operation 302data is received from a device. For example, the edge telecommunicationsnetwork system 100 may receive data from a device 120 a communicatingwith edge compute site 102 a via network 112 a. In general, the data maybe any type of data sent by the device.

Once the data is received, flow proceeds to operation 304, and it isdetermined whether the raw data should be sent to other sites. Inexamples, an orchestration system, such as orchestration system 114, mayhave provided rules and groupings of edge compute sites 102 to thetransport system 104 of the edge compute site 102. The edge compute sitecan determine whether the raw data should be sent to other edge computesites based on the grouping and/or rules. For example, edge compute site102 a determines the raw data should be shared with edge compute site102 b but not edge compute site 102 n based on the type of datareceived. Data may be sent to any number of edge compute sites 102. Inexamples, which edge compute sites 102 receive the data is based on thetype of data received and/or the model associated with the data. Inother examples, which edge compute sites 102 receive the data is basedon the locations of the sites relative to the edge compute site 102 thatreceived the data.

If the edge compute site determines that the raw data should be sent toother sites, flow then proceeds to operation 306 and the data isreplicated. For example, the session systems 106 may replicate the rawdata so that it may be sent to other sites.

In operation 308, the data is sent to the other sites. For example, thereplicated data is sent to the sites the transport system of the edgecompute site determines the data should be sent to.

Once the data is sent to the other sites or it is determined that datashould not be sent to other sites in operation 304, flow proceeds tooperation 310. In operation 310, it is determined what modification tothe data is needed. The modification may be determined, e.g., by thepresentation system 108 so the data may be used to refine a model. Forexample, the presentation system 108 a may determine to extract featurepairs from the data that a particular model can use (e.g., a modelstored by model system 116 d). If raw data was shared with other edgecompute sites 102 b-n, then the presentation systems 108 b-n at thoserespective edge compute sites 102 b-n may determine to extract featurepairs from the data that a particular model can use (e.g., a modelstored by model systems 116 f-i).

Flow proceeds to operation 312, and the data is modified. In examples,the data is modified based on how the presentation system 108 determinesthe data should be modified in operation 310. For example, thepresentation system 108 may extract certain feature pairs from the dataneeded by particular models stored and/or remove unnecessary orirrelevant data. In examples, the raw data may be too large to beefficiently consumed by a model. As such, the data may be modified bysampling the data. For example, if the raw data contains one year ofperformance metrics and event logs for particular computing cards in aplurality of servers in a network, the data may be modified by samplingthe data in intervals (e.g., once a day) for a shorter period and onlyfor certain feature pairs proven to be predictive of performance. Inaddition, the sampling could be done for related cards (e.g., eachfailed card and a corresponding, related healthy card) so that the modelcan be trained to recognize differences between healthy cards andsimilarly situated cards that have (or are predicted to) fail.

Once the data is modified, flow proceeds to operation 314. In operation314, it is determined whether the modified data should be sent to othersites. As explained above, in examples, the modified data may beprovided to other edge compute sites 102 if the sites 102 are refiningthe same model in the same way. Providing the modified data may avoidthe presentation systems 108 of the other edge compute sites 102 fromhaving to modify the data in the same way. In examples, theorchestration system 114 may provide rules and groupings of edge computesites to the transport systems 104 of the edge compute sites 102. Theedge compute site 102 can determine whether the data should be sent toother edge compute sites 102 based on the grouping and/or rules. Forexample, the transport system 104 may have a group that uses thereceived data in the same way, so the transport system determines toshare the modified data.

If it is determined that the modified data should be shared, flowproceeds to operation 316. In operation 316, the modified data isreplicated. The data may be replicated in the same way described abovein operation 306.

Once the modified data is replicated, flow proceeds to operation 318,and the modified data is sent to the other edge compute sites 102. Themodified data may be shared in the same way described above in operation308, e.g., based on rules and groupings of edge compute sites 102provided to the transport systems 104 by an orchestration system 114.

Proceeding to FIG. 3B, once the modified data is sent to other sites orit is determined that the modified data should not be sent to othersites in operation 314, flow proceeds to operation 320. In operation320, it is determined whether the data is relevant to a model at thesite. For example, edge compute site 102 a may determine that the datamay be relevant to a model stored in the model system 116 a of the edgecompute site 102 a.

If it is determined that the data is relevant to a model at the site,flow proceeds to operation 322, and the model is updated. For example,an application stored in the application system 110 a of the edgecompute site 102 a may use the modified data to refine the model storedin model system 116 a. For example, the application system 110 a mayobtain feature pairs to the model for the model to ingest.

Once the model is updated, flow proceeds to operation 324. In operation324, the updated model is sent to one or more devices 120. For example,the orchestration system 114 and/or transport system 104 a of the edgecompute site 102 a may provide indication of which devices (e.g., 120 aand 120 b) use the model. The edge compute site can provide the updatedmodel to those devices 120.

Method 300 may also include operation 326. In operation 326, it isdetermined whether the modified data is relevant to a model at one ormore devices. For example, the modified data may be relevant to devicesin communication with the edge compute site. The orchestration system114 and/or the transport system of the edge compute site 102 maydetermine whether at least one of the devices 120 in communication withthe edge compute site 102 has a model to which the data is relevant. Inexamples, the devices 102 may subscribe to one or more services providedby the application system 110 to receive data relevant to the model(s)used by such device(s) 120. In some examples, if the updated model isprovided to the devices 120 at operation 324, then the modified data maynot be provided to the devices 120. In other examples, the modified datamay be provided to the devices 120 in addition to, or in lieu of, amodified model, and the devices 120 may update the models themselves.

If it is determined that the data is relevant to a model at one or moredevices, the modified data is provided at operation 328 to thedevice(s). In nonexclusive examples, the modified data may be relevantto devices 120 connected to vehicles that use a self-driving model. Inthose examples, the edge compute site 102 may provide the modified datato devices 120 connected to vehicles that use the self-driving model sothat such devices can update their respective models.

In examples, the models can then be used at the devices, and at thesites, to make predictions, e.g., about site and device performance andto potentially take mitigating actions. In other examples, the modelscan be used to provide alerting either to one or more machines and/or tohuman operators. For example, if the models have been trained torecognize network computing cards that are about to fail at certaindevices or sites, an operator may be alerted and/or an automatedprocurement process may be initiated to order a replacement before thecard fails. The procurement process may, for example, includeautomatically ordering the required replacement, shipping the requiredreplacement to the appropriate address, and generating a service ticketto replace the part predicted to fail prior to such failure occurring.In another example, if the model is relevant to a self-drivingapplication, the model can be used to improve object recognition and/orautonomous driving behavior of a vehicle, among other things.

Further, alerting that is performed using the improved models can bebased on specific criteria and be priority-based. For example, oneimplementation of the present systems and methods may include providingautonomous network management. This may include capturing and evaluatingdata related to route congestion, bandwidth availability, latency, andtraffic priorities at different sites and devices comprising, orconnected to, the network. The alerting thresholds may be based on typeof traffic, protocol, and transmission control protocol (TCP) headerflags. For example, extensible messaging and presence protocol (XMPP)traffic carrying voice over internet protocol (VoIP) has a lower latencytolerance than XMPP carrying internet of things (IoT) data. The XMPPprotocol is typically prioritized over hypertext transfer protocolsecure (HTTPS) traffic—because HTTPS has a higher tolerance for latency.The present systems and methods could be used to train models throughoutthe network to automatically perform route optimization—beyond thetraditional network control plane built into the application specificintegrated circuits (ASICs) and software defined networking (SDN)equipment such as routing information protocol (RIP) version 2, openshortest path first (OSPF)—allowing dynamic reconfiguration of virtualswitches within a data center to optimize bandwidth availability.

FIG. 4 is a block diagram illustrating an example of a computing deviceor computer system 400 which may be used in implementing the examples ofthe components of the network disclosed above. For example, edge computesystems 102, orchestration system 114 and/or devices 120, discussedabove may comprise the computing system 400 of FIG. 4 . The computersystem (system) 400 includes one or more processors 402-406. Processors402-406 may include one or more internal levels of cache (not shown) anda bus controller or bus interface unit to direct interaction with theprocessor bus 412. Processor bus 412, also known as the host bus or thefront side bus, may be used to couple the processors 402-406 with thesystem interface 414. System interface 414 may be connected to theprocessor bus 412 to interface other components of the system 400 withthe processor bus 412. For example, system interface 414 may include amemory controller 414 for interfacing a main memory 416 with theprocessor bus 412. The main memory 416 typically includes one or morememory cards and a control circuit (not shown). System interface 414 mayalso include an input/output (I/O) interface 420 to interface one ormore I/O bridges or I/O devices with the processor bus 412. One or moreI/O controllers and/or I/O devices may be connected with the I/O bus426, such as I/O controller 428 and I/O device 430, as illustrated.

I/O device 430 may also include an input device (not shown), such as analphanumeric input device, including alphanumeric and other keys forcommunicating information and/or command selections to the processors402-406. Another type of user input device includes cursor control, suchas a mouse, a trackball, or cursor direction keys for communicatingdirection information and command selections to the processors 402-406and for controlling cursor movement on the display device.

System 400 may include a dynamic storage device, referred to as mainmemory 416, or a random access memory (RAM) or other computer-readabledevices coupled to the processor bus 412 for storing information andinstructions to be executed by the processors 402-406. Main memory 416also may be used for storing temporary variables or other intermediateinformation during execution of instructions by the processors 402-406.System 400 may include a read only memory (ROM) and/or other staticstorage device coupled to the processor bus 412 for storing staticinformation and instructions for the processors 402-406. The system setforth in FIG. 4 is but one possible example of a computer system thatmay employ or be configured in accordance with aspects of the presentdisclosure.

According to one example, the above techniques may be performed bycomputer system 400 in response to processor 404 executing one or moresequences of one or more instructions contained in main memory 416.These instructions may be read into main memory 416 from anothermachine-readable medium, such as a storage device. Execution of thesequences of instructions contained in main memory 416 may causeprocessors 402-406 to perform the process steps described herein. Inalternative examples, circuitry may be used in place of or incombination with the software instructions. Thus, examples of thepresent disclosure may include both hardware and software components.

A machine readable medium includes any mechanism for storing ortransmitting information in a form (e.g., software, processingapplication) readable by a machine (e.g., a computer). Such media maytake the form of, but is not limited to, non-volatile media and volatilemedia and may include removable data storage media, non-removable datastorage media, and/or external storage devices made available through awired or wireless network architecture with such computer programproducts, including one or more database management products, web serverproducts, bare metal server products, and/or other additional softwarecomponents. Examples of removable data storage media include CompactDisc Read-Only Memory (CD-ROM), Digital Versatile Disc Read-Only Memory(DVD-ROM), magneto-optical disks, flash drives, and the like. Examplesof non-removable data storage media include internal magnetic harddisks, SSDs, and the like. The one or more memory devices 406 mayinclude volatile memory (e.g., dynamic random access memory (DRAM),static random access memory (SRAM), etc.) and/or non-volatile memory(e.g., read-only memory (ROM), flash memory, etc.).

Computer program products containing mechanisms to effectuate thesystems and methods in accordance with the presently describedtechnology may reside in main memory 416, which may be referred to asmachine-readable media. It will be appreciated that machine-readablemedia may include any tangible non-transitory medium that is capable ofstoring or encoding instructions to perform any one or more of theoperations of the present disclosure for execution by a machine or thatis capable of storing or encoding data structures and/or modulesutilized by or associated with such instructions. Machine-readable mediamay include a single medium or multiple media (e.g., a centralized ordistributed database, and/or associated caches and servers) that storethe one or more executable instructions or data structures.

Examples of the present disclosure include various steps, which aredescribed in this specification. The steps may be performed by hardwarecomponents or may be embodied in machine-executable instructions, whichmay be used to cause a general-purpose or special-purpose processorprogrammed with the instructions to perform the steps. Alternatively,the steps may be performed by a combination of hardware, software and/orfirmware.

Various modifications and additions can be made to the exemplaryexamples discussed without departing from the scope of the presentinvention. For example, while the examples described above refer toparticular features, the scope of this invention also includes exampleshaving different combinations of features and examples that do notinclude all of the described features. Accordingly, the scope of thepresent invention is intended to embrace all such alternatives,modifications, and variations together with all equivalents thereof.

While specific implementations are discussed, it should be understoodthat this is done for illustration purposes only. A person skilled inthe relevant art will recognize that other components and configurationsmay be used without parting from the spirit and scope of the disclosure.Thus, the following description and drawings are illustrative and arenot to be construed as limiting. Numerous specific details are describedto provide a thorough understanding of the disclosure. However, incertain instances, well-known or conventional details are not describedin order to avoid obscuring the description. References to one or anexample in the present disclosure can be references to the same exampleor any example; and such references mean at least one of the examples.

Reference to “one example” or “an example” means that a particularfeature, structure, or characteristic described in connection with theexample is included in at least one example of the disclosure. Theappearances of the phrase “in one example” in various places in thespecification are not necessarily all referring to the same example, norare separate or alternative examples mutually exclusive of otherexamples. Moreover, various features are described which may beexhibited by some examples and not by others.

The terms used in this specification generally have their ordinarymeanings in the art, within the context of the disclosure, and in thespecific context where each term is used. Alternative language andsynonyms may be used for any one or more of the terms discussed herein,and no special significance should be placed upon whether or not a termis elaborated or discussed herein. In some cases, synonyms for certainterms are provided. A recital of one or more synonyms does not excludethe use of other synonyms. The use of examples anywhere in thisspecification including examples of any terms discussed herein isillustrative only and is not intended to further limit the scope andmeaning of the disclosure or of any example term. Likewise, thedisclosure is not limited to various examples given in thisspecification.

Without intent to limit the scope of the disclosure, examples ofinstruments, apparatus, methods, and their related results according tothe examples of the present disclosure are given below. Note that titlesor subtitles may be used in the examples for convenience of a reader,which in no way should limit the scope of the disclosure. Unlessotherwise defined, technical and scientific terms used herein have themeaning as commonly understood by one of ordinary skill in the art towhich this disclosure pertains. In the case of conflict, the presentdocument, including definitions will control.

Additional features and advantages of the disclosure are set forth inthe description, and in part will be obvious from the description, orcan be learned by practice of the herein disclosed principles. Thefeatures and advantages of the disclosure can be realized and obtainedby means of the instruments and combinations particularly pointed out inthe appended claims. These and other features of the disclosure willbecome more fully apparent from the following description and appendedclaims or can be learned by the practice of the principles set forthherein.

We claim:
 1. A method, comprising: receiving, at a first edge computesite of a telecommunications network, raw data from a first device overa first access network; determining, by the first edge compute site,that the raw data needs to be modified for consumption by a first model;modifying the raw data to generate modified data; modifying the firstmodel using the modified data to generate a modified first model;sending the modified first model to at least one device connected to thefirst edge compute site; and using the modified first model toautomatically affect operation of at least one of the first edge computesite or the at least one device.
 2. The method of claim 1, furthercomprising: receiving, by the first edge compute site, second modifieddata related to a second model, from a second edge compute site.
 3. Themethod of claim 2, further comprising: modifying the second model at thefirst edge compute site; and providing, by the first edge compute site,the modified second model to the second device.
 4. The method of claim2, further comprising: evaluating the second modified data to determinea security value for the second modified data; based on determining thesecurity value, determining, by the first compute site, whether toprovide the second modified data to the second device.
 5. The method ofclaim 2, further comprising: providing the second modified data by thefirst edge compute site to the second device over the first accessnetwork.
 6. The method of claim 1, further comprising: receiving, from anetwork orchestration system at the first edge compute site,instructions to provide the modified data to a second edge compute siteand the modified first model to the second device.
 7. The method ofclaim 6, further comprising: receiving updated instructions from thenetwork orchestration system at the first edge compute site to no longerprovide updates to modified data or the modified first model to thesecond edge compute site.
 8. The method of claim 6, further comprising:receiving, from the network orchestration system at the first edgecompute site, instructions to provide the raw data to a third edgecompute site but not provide the raw data to the second edge computesite.
 9. The method of claim 1, wherein modifying the raw data comprisesextracting feature pairs from the raw data.
 10. The method of claim 9,wherein modifying the raw data to generate the modified data comprisesgenerating first modified data by extracting a first set of featurepairs from the raw data for use in the first model and generating secondmodified data by extracting a second set of feature pairs from the rawdata for use in a second model, the method further comprising: using thefirst modified data to modify the first model at the first edge computesite; and sending the second modified data to a second edge computesite.
 11. A system, comprising: at least one processor; memory,operatively connected to the at least one processor and storinginstructions that, when executed by the at least one processor, causethe system to perform a method, the method comprising: receiving, at afirst edge compute site of a telecommunications network, raw data from afirst device over a first access network; determining, by the first edgecompute site, that the raw data needs to be modified for consumption bya first model; modifying the raw data to generate modified data;modifying the first model using the modified data to generate a modifiedfirst model; sending the modified first model to at least one deviceconnected to the first edge compute site; and using the modified firstmodel to automatically affect operation of at least one of the firstedge compute site or the at least one device.
 12. The system of claim11, wherein the method further comprises: receiving, by the first edgecompute site, second modified data related to a second model, from asecond edge compute site.
 13. The system of claim 12, wherein the methodfurther comprises: modifying the second model at the first edge computesite; and providing, by the first edge compute site, the modified secondmodel to the second device.
 14. The system of claim 12, wherein themethod further comprises: evaluating the second modified data todetermine a security value for the second modified data; based ondetermining the security value, determining, by the first compute site,whether to provide the second modified data to the second device. 15.The system of claim 12, wherein the method further comprises: providingthe second modified data by the first edge compute site to the seconddevice over the first access network.
 16. The system of claim 12,wherein the method further comprises: receiving, from a networkorchestration system at the first edge compute site, instructions toprovide the modified data to the second edge compute site and themodified first model to the second device.
 17. The system of claim 16,wherein the method further comprises: receiving updated instructionsfrom the network orchestration system at the first edge compute site tono longer provide updates to modified data or the modified first modelto the second edge compute site.
 18. The system of claim 16, wherein themethod further comprises: receiving, from the network orchestrationsystem at the first edge compute site, instructions to provide the rawdata to a third edge compute site but not provide the raw data to thesecond edge compute site.
 19. The system of claim 11, wherein modifyingthe raw data comprises extracting feature pairs from the raw data,wherein modifying the raw data to generate the modified data comprisesgenerating first modified data by extracting a first set of featurepairs from the raw data for use in the first model and generating secondmodified data by extracting a second set of feature pairs from the rawdata for use in a second model, and wherein the method furthercomprises: using the first modified data to modify the first model atthe first edge compute site; and sending the second modified data to asecond edge compute site.